OpenRaiser/Intern-Atlas huggingface dataset:
TextSAM-EUS: Text Prompt Learning for SAM to Accurately Segment Pancreatic Tumor in Endoscopic Ultrasound
Venue: arXiv cs.CV 2025
Year: 2025

Pancreatic cancer carries a poor prognosis and relies on endoscopic ultrasound (EUS) for targeted biopsy and radiotherapy. However, the speckle noise, low contrast, and unintuitive appearance of EUS make segmentation of pancreatic tumors with fully supervised deep learning (DL) models both error-prone and dependent on large, expert-curated annotation datasets. To address these challenges, we present TextSAM-EUS, a novel, lightweight, text-driven adaptation of the Segment Anything Model (SAM) that requires no manual geometric prompts at inference. Our approach leverages text prompt learning (context optimization) through the BiomedCLIP text encoder in conjunction with a LoRA-based adaptation of SAM's architecture to enable automatic pancreatic tumor segmentation in EUS, tuning only 0.86% of the total parameters. On the public Endoscopic Ultrasound Database of the Pancreas, TextSAM-EUS with automatic prompts attains 82.69% Dice and 85.28% normalized surface distance (NSD), and with manual geometric prompts reaches 83.10% Dice and 85.70% NSD, outperforming both existing state-of-the-art (SOTA) supervised DL models and foundation models (e.g., SAM and its variants). As the first attempt to incorporate prompt learning in SAM-based medical image segmentation, TextSAM-EUS offers a practical option for efficient and robust automatic EUS segmentation. Code is available at https://github.com/HealthX-Lab/TextSAM-EUS .
Agentic AI framework for End-to-End Medical Data Inference
Venue: arXiv cs.AI 2025
Year: 2025

Building and deploying machine learning solutions in healthcare remains expensive and labor-intensive due to fragmented preprocessing workflows, model compatibility issues, and stringent data privacy constraints. In this work, we introduce an Agentic AI framework that automates the entire clinical data pipeline, from ingestion to inference, through a system of modular, task-specific agents. These agents handle both structured and unstructured data, enabling automatic feature selection, model selection, and preprocessing recommendation without manual intervention. We evaluate the system on publicly available datasets from geriatrics, palliative care, and colonoscopy imaging. For example, in the case of structured data (anxiety data) and unstructured data (colonoscopy polyps data), the pipeline begins with file-type detection by the Ingestion Identifier Agent, followed by the Data Anonymizer Agent ensuring privacy compliance, where we first identify the data type and then anonymize it. The Feature Extraction Agent identifies features using an embedding-based approach for tabular data, extracting all column names, and a multi-stage MedGemma-based approach for image data, which infers modality and disease name. These features guide the Model-Data Feature Matcher Agent in selecting the best-fit model from a curated repository. The Preprocessing Recommender Agent and Preprocessing Implementor Agent then apply tailored preprocessing based on data type and model requirements. Finally, the ``Model Inference Agent" runs the selected model on the uploaded data and generates interpretable outputs using tools like SHAP, LIME, and DETR attention maps. By automating these high-friction stages of the ML lifecycle, the proposed framework reduces the need for repeated expert intervention, offering a scalable, cost-efficient pathway for operationalizing AI in clinical environments.
Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning
Venue: arXiv cs.LG 2025
Year: 2025

Recent work has shown that language models can self-improve by maximizing their own confidence in their predictions, without relying on external verifiers or reward signals. In this work, we study the test-time scaling of language models for mathematical reasoning tasks, where the model's own confidence is used to select the most promising attempts. Surprisingly, we find that we can achieve significant performance gains by continuing only the most promising attempt, selected by the model's prefix-confidence. We systematically evaluate prefix-confidence scaling on five mathematical reasoning datasets: the school-level GSM8K and MATH500, and the competition-level AMC23, AIME24, and AIME25. We find that prefix-confidence scaling with prefixes of only 32 tokens achieves a better accuracy-compute trade-off than majority voting. Moreover, prefix-confidence scaling appears less susceptible than BoN to length biases. Finally, we also evaluate test-time training with prefix-confidence and find that, while outperforming the base model, it does not improve over prefix-confidence scaling.
HIVMedQA: Benchmarking large language models for HIV medical decision support
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) are emerging as valuable tools to support clinicians in routine decision-making. HIV management is a compelling use case due to its complexity, including diverse treatment options, comorbidities, and adherence challenges. However, integrating LLMs into clinical practice raises concerns about accuracy, potential harm, and clinician acceptance. Despite their promise, AI applications in HIV care remain underexplored, and LLM benchmarking studies are scarce. This study evaluates the current capabilities of LLMs in HIV management, highlighting their strengths and limitations. We introduce HIVMedQA, a benchmark designed to assess open-ended medical question answering in HIV care. The dataset consists of curated, clinically relevant questions developed with input from an infectious disease physician. We evaluated seven general-purpose and three medically specialized LLMs, applying prompt engineering to enhance performance. Our evaluation framework incorporates both lexical similarity and an LLM-as-a-judge approach, extended to better reflect clinical relevance. We assessed performance across key dimensions: question comprehension, reasoning, knowledge recall, bias, potential harm, and factual accuracy. Results show that Gemini 2.5 Pro consistently outperformed other models across most dimensions. Notably, two of the top three models were proprietary. Performance declined as question complexity increased. Medically fine-tuned models did not always outperform general-purpose ones, and larger model size was not a reliable predictor of performance. Reasoning and comprehension were more challenging than factual recall, and cognitive biases such as recency and status quo were observed. These findings underscore the need for targeted development and evaluation to ensure safe, effective LLM integration in clinical care.
Logical Characterizations of GNNs with Mean Aggregation
Venue: arXiv cs.AI 2025
Year: 2025

We study the expressive power of graph neural networks (GNNs) with mean as the aggregation function, with the following results. In the non-uniform setting, such GNNs have exactly the same expressive power as ratio modal logic, which has modal operators expressing that at least a certain ratio of the successors of a vertex satisfies a specified property. In the uniform setting, the expressive power relative to MSO is exactly that of modal logic, and thus identical to the (absolute) expressive power of GNNs with max aggregation. The proof, however, depends on constructions that are not satisfactory from a practical perspective. This leads us to making the natural assumptions that combination functions are continuous and classification functions are thresholds. The resulting class of GNNs with mean aggregation turns out to be much less expressive: relative to MSO and in the uniform setting, it has the same expressive power as alternation-free modal logic. This is in contrast to the expressive power of GNNs with max and sum aggregation, which is not affected by these assumptions.
An Improved ChaCha Algorithm Based on Quantum Random Number
Venue: arXiv cs.CR 2025
Year: 2025

Due to the merits of high efficiency and strong security against timing and side-channel attacks, ChaCha has been widely applied in real-time communication and data streaming scenarios. However, with the rapid development of AI-assisted cryptanalysis and quantum computing technologies, there are serious challenges to the secure implementation of ChaCha cipher. To further strengthen the security of ChaCha cipher, we propose an improved variant based on quantum random numbers, i.e., Quantum Random Number Enhanced ChaCha (QRE-ChaCha). Specifically, the design XORs the initial constants with quantum random numbers and periodically injects quantum random numbers into selected state words during odd rounds to enhance diffusion. Compared with the original ChaCha, the present variant shows stronger resistance to differential attacks and generates a keystream with statistical randomness, thereby offering increased robustness against both classical and quantum attacks. To evaluate the security and performance of the present ChaCha, our analysis proceeds in three main parts. Firstly, we analyze its theoretical security in terms of quantum randomness and attack testing, and conduct differential cryptanalysis with an automated search method based on the Boolean satisfiability problem (SAT). Secondly, we subject the keystream generated by the cipher to randomness tests using the NIST statistical test suite and the GM/T 0005-2021 randomness testing standard. Finally, we assess its encryption and decryption performance by measuring its encryption speed on files of various sizes. According to the results, the present ChaCha is significantly improved to resist differential attacks while maintaining the high efficiency of the original ChaCha cipher, and its keystream successfully passes statistical randomness tests using the NIST and GM/T 0005-2021 standards, meeting cryptographic application requirements.
SMECS: A Software Metadata Extraction and Curation Software
Venue: arXiv cs.SE 2025
Year: 2025

Metadata play a crucial role in adopting the FAIR principles for research software and enables findability and reusability. However, creating high-quality metadata can be resource-intensive for researchers and research software engineers. To address this challenge, we developed the Software Metadata Extraction and Curation Software (SMECS) which integrates the extraction of metadata from existing sources together with a user-friendly interface for metadata curation. SMECS extracts metadata from online repositories such as GitHub and presents it to researchers through an interactive interface for further curation and export as a CodeMeta file. The usability of SMECS was evaluated through usability experiments which confirmed that SMECS provides a satisfactory user experience. SMECS supports the FAIRification of research software by simplifying metadata creation.
Differential-UMamba: Rethinking Tumor Segmentation Under Limited Data Scenarios
Venue: arXiv cs.CV 2025
Year: 2025

In data-scarce scenarios, deep learning models often overfit to noise and irrelevant patterns, which limits their ability to generalize to unseen samples. To address these challenges in medical image segmentation, we introduce Diff-UMamba, a novel architecture that combines the UNet framework with the mamba mechanism to model long-range dependencies. At the heart of Diff-UMamba is a noise reduction module, which employs a signal differencing strategy to suppress noisy or irrelevant activations within the encoder. This encourages the model to filter out spurious features and enhance task-relevant representations, thereby improving its focus on clinically significant regions. As a result, the architecture achieves improved segmentation accuracy and robustness, particularly in low-data settings. Diff-UMamba is evaluated on multiple public datasets, including medical segmentation decathalon dataset (lung and pancreas) and AIIB23, demonstrating consistent performance gains of 1-3% over baseline methods in various segmentation tasks. To further assess performance under limited data conditions, additional experiments are conducted on the BraTS-21 dataset by varying the proportion of available training samples. The approach is also validated on a small internal non-small cell lung cancer dataset for the segmentation of gross tumor volume in cone beam CT, where it achieves a 4-5% improvement over baseline.
TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks
Venue: arXiv cs.CL 2025
Year: 2025

Root Cause Analysis (RCA) in telecommunication networks is a critical task, yet it presents a formidable challenge for Artificial Intelligence (AI) due to its complex, graph-based reasoning requirements and the scarcity of realistic benchmarks.
Covert Communications in MEC-Based Networked ISAC Systems Towards Low-Altitude Economy
Venue: arXiv cs.IT 2025
Year: 2025

Low-altitude economy (LAE) is an emerging business model, which heavily relies on integrated sensing and communications (ISAC), mobile edge computing (MEC), and covert communications. This paper investigates the convert transmission design in MEC-based networked ISAC systems towards LAE, where an MEC server coordinates multiple access points to simultaneously receive computation tasks from multiple unmanned aerial vehicles (UAVs), locate a target in a sensing area, and maintain UAVs' covert transmission against multiple wardens. We first derive closed-form expressions for the detection error probability (DEP) at wardens. Then, we formulate a total energy consumption minimization problem by optimizing communication, sensing, and computation resources as well as UAV trajectories, subject to the requirements on quality of MEC services, DEP, and radar signal-to-interference-and-noise ratio, and the causality of UAV trajectories. An alternating optimization based algorithm is proposed to handle the considered problem, which decomposes it into two subproblems: joint optimization of communication, sensing, and computation resources, and UAV trajectory optimization. The former is addressed by a successive convex approximation based algorithm, while the latter is solved via a trust-region based algorithm. Simulations validate the effectiveness of the proposed algorithm compared with various benchmarks, and reveal the trade-offs among communication, sensing, and computation in LAE systems.
Comparing Non-minimal Semantics for Disjunction in Answer Set Programming
Venue: arXiv cs.AI 2025
Year: 2025

In this paper, we compare four different semantics for disjunction in Answer Set Programming that, unlike stable models, do not adhere to the principle of model minimality. Two of these approaches, Cabalar and Muñiz' \emph{Justified Models} and Doherty and Szalas' \emph{Strongly Supported Models}, directly provide an alternative non-minimal semantics for disjunction. The other two, Aguado et al's \emph{Forks} and Shen and Eiter's \emph{Determining Inference} (DI) semantics, actually introduce a new disjunction connective, but are compared here as if they constituted new semantics for the standard disjunction operator. We are able to prove that three of these approaches (Forks, Justified Models and a reasonable relaxation of the DI semantics) actually coincide, constituting a common single approach under different definitions. Moreover, this common semantics always provides a superset of the stable models of a program (in fact, modulo any context) and is strictly stronger than the fourth approach (Strongly Supported Models), that actually treats disjunctions as in classical logic.
DepthDark: Robust Monocular Depth Estimation for Low-Light Environments
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, foundation models for monocular depth estimation have received increasing attention. Current methods mainly address typical daylight conditions, but their effectiveness notably decreases in low-light environments. There is a lack of robust foundational models for monocular depth estimation specifically designed for low-light scenarios. This largely stems from the absence of large-scale, high-quality paired depth datasets for low-light conditions and the effective parameter-efficient fine-tuning (PEFT) strategy. To address these challenges, we propose DepthDark, a robust foundation model for low-light monocular depth estimation. We first introduce a flare-simulation module and a noise-simulation module to accurately simulate the imaging process under nighttime conditions, producing high-quality paired depth datasets for low-light conditions. Additionally, we present an effective low-light PEFT strategy that utilizes illumination guidance and multiscale feature fusion to enhance the model's capability in low-light environments. Our method achieves state-of-the-art depth estimation performance on the challenging nuScenes-Night and RobotCar-Night datasets, validating its effectiveness using limited training data and computing resources.
Multimodal Behavioral Patterns Analysis with Eye-Tracking and LLM-Based Reasoning
Venue: arXiv cs.HC 2025
Year: 2025

Eye-tracking data reveals valuable insights into users' cognitive states but is difficult to analyze due to its structured, non-linguistic nature. While large language models (LLMs) excel at reasoning over text, they struggle with temporal and numerical data. This paper presents a multimodal human-AI collaborative framework designed to enhance cognitive pattern extraction from eye-tracking signals. The framework includes: (1) a multi-stage pipeline using horizontal and vertical segmentation alongside LLM reasoning to uncover latent gaze patterns; (2) an Expert-Model Co-Scoring Module that integrates expert judgment with LLM output to generate trust scores for behavioral interpretations; and (3) a hybrid anomaly detection module combining LSTM-based temporal modeling with LLM-driven semantic analysis. Our results across several LLMs and prompt strategies show improvements in consistency, interpretability, and performance, with up to 50% accuracy in difficulty prediction tasks. This approach offers a scalable, interpretable solution for cognitive modeling and has broad potential in adaptive learning, human-computer interaction, and educational analytics.
Countering Privacy Nihilism
Venue: arXiv cs.CY 2025
Year: 2025

Of growing concern in privacy scholarship is artificial intelligence (AI), as a powerful producer of inferences. Taken to its limits, AI may be presumed capable of inferring "everything from everything," thereby making untenable any normative scheme, including privacy theory and privacy regulation, which rests on protecting privacy based on categories of data - sensitive versus non-sensitive, private versus public. Discarding data categories as a normative anchoring in privacy and data protection as a result of an unconditional acceptance of AI's inferential capacities is what we call privacy nihilism. An ethically reasoned response to AI inferences requires a sober consideration of AI capabilities rather than issuing an epistemic carte blanche. We introduce the notion of conceptual overfitting to expose how privacy nihilism turns a blind eye toward flawed epistemic practices in AI development. Conceptual overfitting refers to the adoption of norms of convenience that simplify the development of AI models by forcing complex constructs to fit data that are conceptually under-representative or even irrelevant. While conceptual overfitting serves as a helpful device to counter normative suggestions grounded in hyperbolic AI capability claims, AI inferences shake any privacy regulation that hinges protections based on restrictions around data categories. We propose moving away from privacy frameworks that focus solely on data type, neglecting all other factors. Theories like contextual integrity evaluate the normative value of privacy across several parameters, including the type of data, the actors involved in sharing it, and the purposes for which the information is used.
ReSemAct: Advancing Fine-Grained Robotic Manipulation via Semantic Structuring and Affordance Refinement
Venue: arXiv cs.RO 2025
Year: 2025

Fine-grained robotic manipulation requires grounding natural language into appropriate affordance targets. However, most existing methods driven by foundation models often compress rich semantics into oversimplified affordances, preventing exploitation of implicit semantic information. To address these challenges, we present ReSemAct, a novel unified manipulation framework that introduces Semantic Structuring and Affordance Refinement (SSAR), powered by the automated synergistic reasoning between Multimodal Large Language Models (MLLMs) and Vision Foundation Models (VFMs). Specifically, the Semantic Structuring module derives a unified semantic affordance description from natural language and RGB observations, organizing affordance regions, implicit functional intent, and coarse affordance anchors into a structured representation for downstream refinement. Building upon this specification, the Affordance Refinement strategy instantiates two complementary flows that separately specialize geometry and position, yielding fine-grained affordance targets. These refined targets are then encoded as real-time joint-space optimization objectives, enabling reactive and robust manipulation in dynamic environments. Extensive simulation and real-world experiments are conducted in semantically rich household and sparse chemical lab environments. The results demonstrate that ReSemAct performs diverse tasks under zero-shot conditions, showcasing the robustness of SSAR with foundation models in fine-grained manipulation. Code and videos at https://github.com/scy-v/ReSemAct and https://resemact.github.io.
Scheduzz: Constraint-based Fuzz Driver Generation with Dual Scheduling
Venue: arXiv cs.SE 2025
Year: 2025

Fuzzing a library requires experts to understand the library usage well and craft high-quality fuzz drivers, which is tricky and tedious. Therefore, many techniques have been proposed to automatically generate fuzz drivers. However, they fail to generate rational fuzz drivers due to the lack of adherence to proper library usage conventions, such as ensuring a resource is closed after being opened. To make things worse, existing library fuzzing techniques unconditionally execute each driver, resulting in numerous irrational drivers that waste computational resources while contributing little coverage and generating false positive bug reports. To tackle these challenges, we propose a novel automatic library fuzzing technique, Scheduzz, an LLM-based library fuzzing technique. It leverages LLMs to understand rational usage of libraries and extract API combination constraints. To optimize computational resource utilization, a dual scheduling framework is implemented to efficiently manage API combinations and fuzz drivers. The framework models driver generation and the corresponding fuzzing campaign as an online optimization problem. Within the scheduling loop, multiple API combinations are selected to generate fuzz drivers, while simultaneously, various optimized fuzz drivers are scheduled for execution or suspension. We implemented Scheduzz and evaluated it in 33 real-world libraries. Compared to baseline approaches, Scheduzz significantly reduces computational overhead and outperforms UTopia on 16 out of 21 libraries. It achieves 1.62x, 1.50x, and 1.89x higher overall coverage than the state-of-the-art techniques CKGFuzzer, Promptfuzz, and the handcrafted project OSS-Fuzz, respectively. In addition, Scheduzz discovered 33 previously unknown bugs in these well-tested libraries, 3 of which have been assigned CVEs.